Developed in collaboration with a number of partner universities and NHS Digital, this new model could be applied in a variety of health and care settings, including supporting GPs and specialists in consultations with their patients to provide more targeted advice based on individual levels of risk.
The model could also be used to inform mathematical modelling of the potential impact of national public health policies on shielding and preventing infection and potentially help identify those at highest risk to be vaccinated, when available.
Principal Investigator, Professor Julia Hippisley-Cox, Professor of Epidemiology and General Practice at the University of Oxford’s Nuffield Department of Primary Care Health Sciences said:
‘Driven by real patient data, this risk assessment tool could enable a more sophisticated approach to identifying and managing those most at risk of infection and more serious COVID-19 disease.’
‘Importantly, it will provide better information for GPs to identify and verify individuals in the community who, in consultation with their doctor, may take steps to reduce their risk, or may be advised to shield.’
In the UK, government guidance on COVID-19 identifies individuals based on three broad categories of risk, with those who are ‘clinically extremely vulnerable’ to the disease previously being advised to shield themselves from the virus.
The model will use routinely collected anonymized electronic health records of 8 million adults in the UK, accessed through the University of Oxford’s QResearch database and linked datasets, to identify factors that can be used to predict those at highest risk of infection and serious illness from COVID-19. These include age, sex, ethnicity, deprivation, smoking status, body mass index, pre-existing medical conditions and current medications.
Algorithms from the data analysis will be developed in conjunction with clinical and data experts at NHS Digital and will drive a clinical risk prediction model which can be applied across various health and care settings. Individualised risk assessment could be used to improve shared decision-making between clinicians and patients based on more accurate information, as well as discussions on how to reduce risk.
Professor Keith Channon, Deputy Head of Medical Sciences, University of Oxford, and Director of Oxford Academic Health Partners, adds:
‘Combining leading expertise in clinical epidemiology and analytical techniques with very large sets of NHS clinical data to develop this new tool illustrates the power of our University and NHS researchers working together, to benefit people at risk of COVID-19.’
The project was a commission from the Office of the Chief Medical Officer for England to NERVTAG (New and Emerging Respiratory Virus Threats Advisory Group), who established the parameters and brought together the team as a sub-group of NERVTAG.
This team is led by the University of Oxford and includes researchers from the universities of Cambridge, Edinburgh, Swansea, Leicester, Nottingham and Liverpool with the London School of Hygiene and Tropical Medicine, Queen’s University Belfast, Queen Mary University of London, University College London, the Department of Health and Social Care, NHS Digital and NHS England.
The research team are planning to utilise other datasets from across all four nations of the UK to validate their model and offer a unified approach to evidence-based risk stratification policy.
Chief Medical Officer for England, Professor Chris Whitty, said:
‘The level of threat posed by COVID-19 varies across the population, and as more is learned about the disease and the risk factors involved, we can start to make risk assessment more nuanced. When developed, this risk prediction tool will improve our ability to target shielding, if it is needed, to those most at risk.’
The research is funded by the National Institute for Health Research Oxford Biomedical Research Centre, and the University of Oxford COVID-19 Rapid Response Fund with support from Wellcome and Cancer Research UK.